OpenRaiser/Intern-Atlas huggingface dataset:
Adversarial Spheres
Venue: arXiv cs.CV 2018
Year: 2018

State of the art computer vision models have been shown to be vulnerable to small adversarial perturbations of the input. In other words, most images in the data distribution are both correctly classified by the model and are very close to a visually similar misclassified image. Despite substantial research interest, the cause of the phenomenon is still poorly understood and remains unsolved. We hypothesize that this counter intuitive behavior is a naturally occurring result of the high dimensional geometry of the data manifold. As a first step towards exploring this hypothesis, we study a simple synthetic dataset of classifying between two concentric high dimensional spheres. For this dataset we show a fundamental tradeoff between the amount of test error and the average distance to nearest error. In particular, we prove that any model which misclassifies a small constant fraction of a sphere will be vulnerable to adversarial perturbations of size $O(1/\sqrt{d})$. Surprisingly, when we train several different architectures on this dataset, all of their error sets naturally approach this theoretical bound. As a result of the theory, the vulnerability of neural networks to small adversarial perturbations is a logical consequence of the amount of test error observed. We hope that our theoretical analysis of this very simple case will point the way forward to explore how the geometry of complex real-world data sets leads to adversarial examples.
Rogue Signs: Deceiving Traffic Sign Recognition with Malicious Ads and Logos
Venue: arXiv cs.CR 2018
Year: 2018

We propose a new real-world attack against the computer vision based systems of autonomous vehicles (AVs). Our novel Sign Embedding attack exploits the concept of adversarial examples to modify innocuous signs and advertisements in the environment such that they are classified as the adversary's desired traffic sign with high confidence. Our attack greatly expands the scope of the threat posed to AVs since adversaries are no longer restricted to just modifying existing traffic signs as in previous work. Our attack pipeline generates adversarial samples which are robust to the environmental conditions and noisy image transformations present in the physical world. We ensure this by including a variety of possible image transformations in the optimization problem used to generate adversarial samples. We verify the robustness of the adversarial samples by printing them out and carrying out drive-by tests simulating the conditions under which image capture would occur in a real-world scenario. We experimented with physical attack samples for different distances, lighting conditions and camera angles. In addition, extensive evaluations were carried out in the virtual setting for a variety of image transformations. The adversarial samples generated using our method have adversarial success rates in excess of 95% in the physical as well as virtual settings.
Minimum Throughput Maximization in UAV-Aided Wireless Powered Communication Networks
Venue: arXiv cs.IT 2018
Year: 2018

This paper investigates unmanned aerial vehicle (UAV)-aided wireless powered communication network (WPCN) systems where a mobile access point (AP) at the UAV serves multiple energy-constrained ground terminals (GTs). Specifically, the UAVs first charge the GTs by transmitting the wireless energy transfer (WET) signals in the downlink. Then, by utilizing the harvested wireless energy from the UAVs, the GTs send their uplink wireless information transmission (WIT) signals to the UAVs. In this paper, depending on the operations of the UAVs, we adopt two different scenarios, namely integrated UAV and separated UAV WPCNs. First, in the integrated UAV WPCN, a UAV acts as a hybrid AP in which both energy transfer and information reception are processed at a single UAV. In contrast, for the separated UAV WPCN, we consider two UAVs each of which behaves as an energy AP and an information AP independently, and thus the energy transfer and the information decoding are separately performed at two different UAVs. For both systems, we jointly optimize the trajectories of the UAVs, the uplink power control, and the time resource allocation for the WET and the WIT to maximize the minimum throughput of the GTs. Since the formulated problems are non-convex, we apply the concave-convex procedure by deriving appropriate convex bounds for non-convex constraints. As a result, we propose iterative algorithms which efficiently identify a local optimal solution for the minimum throughput maximization problems. Simulation results verify the efficiency of the proposed algorithms compared to conventional schemes.
Wi-Fi Teeter-Totter: Overclocking OFDM for Internet of Things
Venue: arXiv cs.NI 2018
Year: 2018

The conventional high-speed Wi-Fi has recently become a contender for low-power Internet-of-Things (IoT) communications. OFDM continues its adoption in the new IoT Wi-Fi standard due to its spectrum efficiency that can support the demand of massive IoT connectivity. While the IoT Wi-Fi standard offers many new features to improve power and spectrum efficiency, the basic physical layer (PHY) structure of transceiver design still conforms to its conventional design rationale where access points (AP) and clients employ the same OFDM PHY. In this paper, we argue that current Wi-Fi PHY design does not take full advantage of the inherent asymmetry between AP and IoT. To fill the gap, we propose an asymmetric design where IoT devices transmit uplink packets using the lowest power while pushing all the decoding burdens to the AP side. Such a design utilizes the sufficient power and computational resources at AP to trade for the transmission (TX) power of IoT devices. The core technique enabling this asymmetric design is that the AP takes full power of its high clock rate to boost the decoding ability. We provide an implementation of our design and show that it can reduce the IoT's TX power by boosting the decoding capability at the receivers.
Riemannian Motion Policies
Venue: arXiv cs.RO 2018
Year: 2018

We introduce the Riemannian Motion Policy (RMP), a new mathematical object for modular motion generation. An RMP is a second-order dynamical system (acceleration field or motion policy) coupled with a corresponding Riemannian metric. The motion policy maps positions and velocities to accelerations, while the metric captures the directions in the space important to the policy. We show that RMPs provide a straightforward and convenient method for combining multiple motion policies and transforming such policies from one space (such as the task space) to another (such as the configuration space) in geometrically consistent ways. The operators we derive for these combinations and transformations are provably optimal, have linearity properties making them agnostic to the order of application, and are strongly analogous to the covariant transformations of natural gradients popular in the machine learning literature. The RMP framework enables the fusion of motion policies from different motion generation paradigms, such as dynamical systems, dynamic movement primitives (DMPs), optimal control, operational space control, nonlinear reactive controllers, motion optimization, and model predictive control (MPC), thus unifying these disparate techniques from the literature. RMPs are easy to implement and manipulate, facilitate controller design, simplify handling of joint limits, and clarify a number of open questions regarding the proper fusion of motion generation methods (such as incorporating local reactive policies into long-horizon optimizers). We demonstrate the effectiveness of RMPs on both simulation and real robots, including their ability to naturally and efficiently solve complicated collision avoidance problems previously handled by more complex planners.
Channel Estimation with Systematic Polar Codes
Venue: arXiv cs.IT 2018
Year: 2018

Study of polar codes in fading channels is of great importance when applying polar codes in wireless communications. Channel estimation is a fundamental step for communication to be possible in fading channels. For both systematic and non-systematic polar codes, construction of them is based on an information set and the known frozen bits. Efficient implementation of systematic and non-systematic polar codes exists. When it comes to channel estimation or channel tracking, additional pilot symbols are inserted in the codeword traditionally. In this paper, to improve the performance of polar codes in the finite domain, pilot symbols are selected from the coded symbols themselves. In order to keep the existing efficient structure of polar code encoding, pilot selection is critical since not all selections can reuse the existing structure. In this paper, two pilot selections denoted as Uneven Pilot Selection (UEPS) and Even Pilot Selection (EPS) are proposed, which do not change the efficient polar encoding structure. The proposed UEPS and EPS is proven to satisfy the efficient construction condition. The performance of EPS is shown in this paper to outperform both the UEPS and the traditional pilot insertion scheme. Simulation results are provided which verify the performance of the proposed pilot selection schemes.
Optimal Content Replication and Request Matching in Large Caching Systems
Venue: arXiv cs.PF 2018
Year: 2018

We consider models of content delivery networks in which the servers are constrained by two main resources: memory and bandwidth. In such systems, the throughput crucially depends on how contents are replicated across servers and how the requests of specific contents are matched to servers storing those contents. In this paper, we first formulate the problem of computing the optimal replication policy which if combined with the optimal matching policy maximizes the throughput of the caching system in the stationary regime. It is shown that computing the optimal replication policy for a given system is an NP-hard problem. A greedy replication scheme is proposed and it is shown that the scheme provides a constant factor approximation guarantee. We then propose a simple randomized matching scheme which avoids the problem of interruption in service of the ongoing requests due to re-assignment or repacking of the existing requests in the optimal matching policy. The dynamics of the caching system is analyzed under the combination of proposed replication and matching schemes. We study a limiting regime, where the number of servers and the arrival rates of the contents are scaled proportionally, and show that the proposed policies achieve asymptotic optimality. Extensive simulation results are presented to evaluate the performance of different policies and study the behavior of the caching system under different service time distributions of the requests.
An Improved Analysis of Least Squares Superposition Codes with Bernoulli Dictionary
Venue: arXiv cs.IT 2018
Year: 2018

For the additive white Gaussian noise channel with average power constraint, sparse superposition codes, proposed by Barron and Joseph in 2010, achieve the capacity. While the codewords of the original sparse superposition codes are made with a dictionary matrix drawn from a Gaussian distribution, we consider the case that it is drawn from a Bernoulli distribution. We show an improved upper bound on its block error probability with least squares decoding, which is fairly simplified and tighter bound than our previous result in 2014.
Systematic design methodology for development and flight testing of a variable pitch quadrotor biplane VTOL UAV for payload delivery
Venue: arXiv cs.SY 2018
Year: 2018

This paper discusses the conceptual design and proof-of-concept flight demonstration of a novel variable pitch quadrotor biplane Unmanned Aerial Vehicle concept for payload delivery. The proposed design combines vertical takeoff and landing (VTOL), precise hover capabilities of a quadrotor helicopter and high range, endurance and high forward cruise speed characteristics of a fixed wing aircraft. The proposed UAV is designed for a mission requirement of carrying and delivering 6 kg payload to a destination at 16 km from the point of origin. First, the design of proprotors is carried out using a physics based modified Blade Element Momentum Theory (BEMT) analysis, which is validated using experimental data generated for the purpose. Proprotors have conflicting requirement for optimal hover and forward flight performance. Next, the biplane wings are designed using simple lifting line theory. The airframe design is followed by power plant selection and transmission design. Finally, weight estimation is carried out to complete the design process. The proprotor design with 24 deg preset angle and -24 deg twist is designed based on 70% weightage to forward flight and 30% weightage to hovering flight conditions. The operating RPM of the proprotors is reduced from 3200 during hover to 2000 during forward flight to ensure optimal performance during cruise flight. The estimated power consumption during forward flight mode is 64% less than that required for hover, establishing the benefit of this hybrid concept. A proof-of-concept scaled prototype is fabricated using commercial-off-the-shelf parts. A PID controller is developed and implemented on the PixHawk board to enable stable hovering flight and attitude tracking.
Utilising Deep Learning and Genome Wide Association Studies for Epistatic-Driven Preterm Birth Classification in African-American Women
Venue: arXiv cs.CE 2018
Year: 2018

Genome Wide Association Studies (GWAS) are used to identify statistically significant genetic variants in case-control studies. GWAS typically use a p-value threshold of 5 x 10-8 to identify highly ranked single nucleotide polymorphisms (SNPs). However, evidence has shown that many of these are, in fact, false positives. Using lower p-values it is possible to to investigate the joint epistatic interactions between SNPs and provide better insights into phenotype expression. However, computational complexity is increased exponentially as a function of higher-order combinations. In this paper, we propose a novel framework, based on nonlinear transformations of combinatorically large SNP data, using stacked autoencoders, to identify higher-order SNP interactions. We focus on the challenging problem of classifying preterm births. Evidence suggests that this complex condition has a strong genetic component with unexplained heritability reportedly between 20%-40%. This claim is substantiated using a GWAS data set, obtained from dbGap, which contains predominantly urban low-income African-American women who had normal deliveries (between 37 and 42 weeks of gestation) and preterm deliveries (less than 37 weeks of gestation). Latent representations from original SNP sequences are used to initialize a deep learning classifier before it is fine-tuned for classification tasks (term and preterm births). The complete network models the epistatic effects of major and minor SNP perturbations. All models are evaluated using standard binary classifier performance metrics. The findings show that important information pertaining to SNPs and epistasis can be extracted from 4666 raw SNPs generated using logistic regression (p-value=5 x 10-3) and used to fit a deep learning model and obtain results (Sen=0.9289, Spec=0.9591, Gini=0.9651, Logloss=0.3080, AUC=0.9825, MSE=0.0942) using 500 hidden nodes.
Asymmetry Hurts: Private Information Retrieval Under Asymmetric Traffic Constraints
Venue: arXiv cs.IT 2018
Year: 2018

We consider the classical setting of private information retrieval (PIR) of a single message (file) out of $M$ messages from $N$ distributed databases under the new constraint of \emph{asymmetric traffic} from databases. In this problem, the \emph{ratios between the traffic} from the databases are constrained, i.e., the ratio of the length of the answer string that the user (retriever) receives from the $n$th database to the total length of all answer strings from all databases is constrained to be $τ_n$. This may happen if the user's access to the databases is restricted due database availability, channel quality to the databases, and other factors. For this problem, for fixed $M$, $N$, we develop a general upper bound $\bar{C}(\boldsymbolτ)$, which generalizes the converse proof of Sun-Jafar, where database symmetry was inherently used. Our converse bound is a piece-wise affine function in the traffic ratio vector $\boldsymbolτ=(τ_1, \cdots, τ_N)$. For the lower bound, we explicitly show the achievability of $\binom{M+N-1}{M}$ corner points. For the remaining traffic ratio vectors, we perform time-sharing between these corner points. The recursive structure of our achievability scheme is captured via a system of difference equations. The upper and lower bounds exactly match for $M=2$ and $M=3$ for any $N$ and any $\boldsymbolτ$. The results show strict loss of PIR capacity due to the asymmetric traffic constraints compared with the symmetric case of Sun-Jafar which implicitly uses $τ_n=\frac{1}{N}$ for all $n$.
A Radio-fingerprinting-based Vehicle Classification System for Intelligent Traffic Control in Smart Cities
Venue: arXiv cs.NI 2018
Year: 2018

The measurement and provision of precise and upto-date traffic-related key performance indicators is a key element and crucial factor for intelligent traffic controls systems in upcoming smart cities. The street network is considered as a highly-dynamic Cyber Physical System (CPS) where measured information forms the foundation for dynamic control methods aiming to optimize the overall system state. Apart from global system parameters like traffic flow and density, specific data such as velocity of individual vehicles as well as vehicle type information can be leveraged for highly sophisticated traffic control methods like dynamic type-specific lane assignments. Consequently, solutions for acquiring these kinds of information are required and have to comply with strict requirements ranging from accuracy over cost-efficiency to privacy preservation. In this paper, we present a system for classifying vehicles based on their radio-fingerprint. In contrast to other approaches, the proposed system is able to provide real-time capable and precise vehicle classification as well as cost-efficient installation and maintenance, privacy preservation and weather independence. The system performance in terms of accuracy and resource-efficiency is evaluated in the field using comprehensive measurements. Using a machine learning based approach, the resulting success ratio for classifying cars and trucks is above 99%.
Statistical Blockage Modeling and Robustness of Beamforming in Millimeter Wave Systems
Venue: arXiv cs.IT 2018
Year: 2018

There has been a growing interest in the commercialization of millimeter wave (mmW) technology as a part of the Fifth-Generation New Radio (5G-NR) wireless standardization efforts. In this direction, many sets of independent measurement campaigns show that wireless propagation at mmW carrier frequencies is only marginally worse than propagation at sub-6 GHz carrier frequencies for small-cell coverage --- one of the most important use-cases for 5G-NR. On the other hand, the biggest determinants of viability of mmW systems in practice are penetration and blockage of mmW signals through different materials in the scattering environment. With this background, the focus of this paper is on understanding the impact of blockage of mmW signals and reduced spatial coverage due to penetration through the human hand, body, vehicles, etc. Leveraging measurements with a 28 GHz mmW experimental prototype and electromagnetic simulation studies, we first propose statistical blockage models to capture the impact of the hand, human body and vehicles. We then study the time-scales at which mmW signals are disrupted by blockage (hand and human body). Our results show that these events can be attributed to physical movements and the time-scales corresponding to blockage are hence on the order of a few 100 ms or more. Building on this fundamental understanding, we finally consider the broader question of robustness of mmW beamforming to handle blockage. Network densification, subarray switching in a user equipment (UE) designed with multiple subarrays, fall back mechanisms such as codebook enhancements and switching to legacy carriers in non-standalone deployments, etc. can address blockage before it leads to a deleterious impact on the mmW link margin.
Instance Scale, Numerical Properties and Design of Metaheuristics: A Study for the Facility Location Problem
Venue: arXiv cs.CE 2018
Year: 2018

Metaheuristics are known to be strong in solving large-scale instances of computationally hard problems. However, their efficiency still needs exploration in the context of instance structure, scale and numerical properties for many of these problems. In this paper, we present an in-depth computational study of two local search metaheuristics for the classical uncapacitated facility location problem. We investigate four problem instance models, studied for the same problem size, for which the two metaheuristics exhibit intriguing and contrasting behaviours. The metaheuristics explored include a local search (LS) algorithm that chooses the best moves in the current neighbourhood, while a randomised local search (RLS) algorithm chooses the first move that does not lead to a worsening. The experimental results indicate that the right choice between these two algorithms depends heavily on the distribution of coefficients within the problem instance. This is also put further into context by finding optimal or near-optimal solutions using a mixed-integer linear programming problem solver. Since the facility location problem is a relatively simple example of a choice-and-assignment problem, similar phenomena are likely to be discovered in a number of other, possibly more complex computational problems in science and engineering.
Blessing of dimensionality: mathematical foundations of the statistical physics of data
Venue: arXiv cs.LG 2018
Year: 2018

The concentration of measure phenomena were discovered as the mathematical background of statistical mechanics at the end of the XIX - beginning of the XX century and were then explored in mathematics of the XX-XXI centuries. At the beginning of the XXI century, it became clear that the proper utilisation of these phenomena in machine learning might transform the curse of dimensionality into the blessing of dimensionality. This paper summarises recently discovered phenomena of measure concentration which drastically simplify some machine learning problems in high dimension, and allow us to correct legacy artificial intelligence systems. The classical concentration of measure theorems state that i.i.d. random points are concentrated in a thin layer near a surface (a sphere or equators of a sphere, an average or median level set of energy or another Lipschitz function, etc.). The new stochastic separation theorems describe the thin structure of these thin layers: the random points are not only concentrated in a thin layer but are all linearly separable from the rest of the set, even for exponentially large random sets. The linear functionals for separation of points can be selected in the form of the linear Fisher's discriminant. All artificial intelligence systems make errors. Non-destructive correction requires separation of the situations (samples) with errors from the samples corresponding to correct behaviour by a simple and robust classifier. The stochastic separation theorems provide us by such classifiers and a non-iterative (one-shot) procedure for learning.